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Computational investigation of circuit mechanisms underlying short-latency responses to cortical stimulation

Transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits a series of high frequency volleys termed D- and I-waves measured epidurally in the corticospinal tract of awake humans. Further, intracortical microstimulation (ICMS) in M1 of non-human primates evokes D- and I-wave responses similar to those observed in TMS. The cortical circuits and mechanisms involved in the…

The study explored the computational models of cortical columns with biophysically-based neurons to understand the mechanisms behind short-latency responses to cortical stimulation. The researchers utilized four models: (1) M1 - single compartment, (2) M1 - multi-compartment, (3) primary auditory cortex (A1) - multi-compartment, and (4) primary somatosensory cortex (S1) - multi-compartment. These models represented the wiring found in each respective cortical region.

The models incorporated the direct effects of stimulation-induced electric fields on pyramidal neurons (PNs) across different layers. The analysis of dose-response curves for layer 5 (L5) PNs revealed that both the M1 and A1 models successfully replicated high-frequency volleys called D- and I-waves. The magnitude of I-waves increased with higher recruitment of layer 2/3 and layer 5 PNs.

The S1-MC model exhibited rhythmic firing activity, but the timing of these events did not align with the experimentally observed I-waves. The models also successfully reproduced the effects of pharmacological agents on I-waves. Furthermore, virtual lesions of specific neural populations across the models provided plausible microcircuit explanations for the first and subsequent I-waves.

By comparing the models across multiple cortical regions, the researchers identified consistent mechanisms underlying the cortical response to TMS. This study contributes to the refinement of computational strategies for optimizing stimulation paradigms.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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